A 0.13-<formula formulatype="inline"><tex Notation="TeX">$\mu{\rm m}$</tex></formula> CMOS Low-Power Capacitor-Less LDO Regulator Using Bulk-Modulation Technique
Bibliographic record
Abstract
In this paper, a bulk-modulation technique is introduced for improving the performance of low-drop-out (LDO) voltage regulators. Compared to conventional LDO voltage regulators, the proposed circuit achieves improved accuracy, stability, and output load current capability. The technique is particularly suited for low-power applications such as biomedical implants and portable devices. A proof-of-concept prototype is designed and fabricated in 0.13-μm CMOS, to illustrate the enhancement that can be achieved by applying this technique. The proposed enhanced LDO regulator which is based on conventional LDO regulators is able to delivers up to 5 mA of load current while providing a 1 V (~ 1.5% load regulation) drawing 99.0 μA from a 1.2 V supply. Measurement results confirm that as compared to conventional LDOs, the proposed circuit offers better stability as well as %75 improvement in the load current delivery and ~10× faster recovery time for no-load to and from full-load transitions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".